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• Academic Tracking: Long-term Effects on College and Career Outcomes

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Keeping Track or Getting Offtrack: Issues in the Tracking Of Students

• pp 1081–1100

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• Lynn M. Mulkey 3 ,
• Sophia Catsambis 4 ,
• Lala Carr Steelman 5 &
• Melanie Hanes-Ramos 3  

Part of the book series: Springer International Handbooks of Education ((SIHE,volume 21))

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Tracking is a generic term that covers ways that most educators keep track of students' academic progress by matriculating them into curricula of varying difficulty. Contests over tracking's practical and theoretical viability — getting offtrack — concern what Oakes (1985) asks about whether tracking makes most children smart or only some smart children smarter? In other words, does the school fairly advance students on the basis of their merits or does it reproduce the inequalities they bring with them at the starting gate (Argys, Rees, & Brewer, 1996; Cohen & Lotan, 1997; Oakes, 1985, 1994; Oakes, Gamoran, & Page, 1992; Slavin, 1987, 1990a, 1990b; Wheelock, 1992)?

While clearly tracking may be a well-intended practice for organizing instruction, international and cross-cultural research fails to support the belief that it improves academic achievement and the debate remains unresolved (Ansalone, 2003; Resh, 1998). In a study of thirty countries the official rationale for tracking is largely based on student ability and not to ascriptive characteristics (Marks, 2005). Other evidence from Palestinian Arab High Schools points to an ongoing disagreement over social stratification within the school remaining largely obscure and requiring further research attention to unravel the tangled threads of the issue (Mazawi, 1998).

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A Social-Cognitive Perspective of the Consequences of Curricular Tracking on Youth Outcomes

Tracking instructional quality across secondary mathematics and English Language Arts classes

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Mulkey, L.M., Catsambis, S., Steelman, L.C., Hanes-Ramos, M. (2009). Keeping Track or Getting Offtrack: Issues in the Tracking Of Students. In: Saha, L.J., Dworkin, A.G. (eds) International Handbook of Research on Teachers and Teaching. Springer International Handbooks of Education, vol 21. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-73317-3_71

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The Troubles with Tracking

Educators have been debating academic tracking since the early years of the public high school.

In recent years a number of school systems have been rethinking the separation of students into different academic tracks. The issue gets at a fundamental question about public schools: should they offer highly specialized coursework, at the cost of replicating existing class structures? Or should they focus on bringing children of all backgrounds together? As education scholar William G. Wraga writes, educators have been debating this issue since the early years of the public high school.

At the turn of the 20th century, Wraga writes, only about one in ten adolescents attended secondary school. The vocational school movement sought to change this, preparing working-class kids for the new jobs that emerged with rising industrialization.

Some of these educators admired Germany’s dual system, which divided 14-year-olds between college preparatory and industrial schools. But others objected strenuously. Vocational education advocate Eugene Davenport argued in 1914 that separate vocational schools would represent a “most powerful step toward the segregation of people according to vocational lines, and from that time it is inevitable that the stratification of society will proceed by leaps and bounds.” Instead, he argued, the goal should be the “cosmopolitan high school,” in which students would spend a quarter of their time specializing in a trade and the rest learning general subjects with a diverse group of peers.

Wraga writes that the model that emerged was somewhere in the middle. With the exception of racially segregated places, students from a range of backgrounds were generally permitted to attend the same high schools. But they were internally divided. Contributing to this trend, during World War I, psychologists introduced new standardized testing methods to divide large groups up by abilities and interests. They brought these same techniques to the nation’s high schools. At a moment when the quantification of all aspects of life was in vogue, this soon added up to the tracking of students into classes at different levels.

In 1926, education theorist George Counts noted that the college preparatory path was understood as prestigious, and typically open to kids from higher-status families, resulting in “attitudes of social inferiority in those who do not pursue the favored curriculums.”

The Great Depression and World War II brought more national soul-searching. One representative 1946 report argued that:

“The American tradition is opposed to the early segregation of students according to intellectual, social or other qualities…. The public school is the only agency in most communities which brings people of all economic, social and religious backgrounds together.”

The tension between unity and specialization continued through the decades of racial desegregation in the South, and Cold War fears about American scientific know-how.

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At the time Wraga was writing, in 1998, increasing interest in school choice options were threatening increasing disruption of the comprehensive high school.

Yet, he argued, high schools remained a key tool for both “providing specialized education that caters to individual needs and interests” and “fostering common sympathies, discourse, and understandings among an increasingly diverse population.”

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• Published: 28 February 2020

Dreams and realities of school tracking and vocational education

• Mahmut Ozer 1 &
• Matjaž Perc   ORCID: orcid.org/0000-0002-3087-541X 2 , 3 , 4  

Palgrave Communications volume  6 , Article number:  34 ( 2020 ) Cite this article

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School tracking has been introduced as a means to provide skills the labor market demands, and as such has been in place for several decades in most Organisation for Economic Co-operation and Development (OECD) countries. The time is thus ripe for a critical review of the effects this has had on the equalities in education and opportunities later in life, and on the quality of vocational education in general. A synthesis of the existing literature reveals gaping holes between the dreams of superior vocational education and training that educational tracking ought to deliver, and the realities of lost opportunities and facilitated inequalities, especially in students with poor socioeconomic background, weak social capital, and sparse social networks. This is all the more true the sooner educational tracking comes into effect. While most OECD countries will start tracking students aged 15 or 16, some countries, such as Germany, will start doing this as early as age 10. Our review shows that this can have catastrophic consequences for students that for various reasons perform poorly early on, as they are indeed unable to recover due to the Matthew effect and preferential attachment in social networks, both of which punish false starts in life and reward first movers. To remedy the situation, we propose educational tracking be held off until later in life, and even then be undertaken with flexibility and late bloomers in mind. We also propose to restructure vocational education by decreasing the degree of curriculum differentiation, by allowing broader vocational education curricula, and by decreasing the number of training occupations in order to account for the changing labor market dynamics.

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Introduction.

Education systems around the world involve a general comprehensive education until specific age levels, following which students are tracked into different school types. Tracking in education, also known as ability grouping, sorting, or differentiation, is used for purposive grouping of students in classrooms of the same school or in different types of schools. This disintegration or the placement of students has a hierarchical structure, which is based on students’ academic performance and/or ability tests (Brunello, 2004 ). The tracking between school types leads to the physical decomposition of students in different schools and creates major differences in curricula. The scale of the curriculum differentiation has a critical role in tracking (Reichelt et al., 2019 ). Although the tracking age varies considerably, even between industrialized countries, the tracking is applied at ages of 15 or 16 in most Organisation for Economic Co-operation and Development (OECD) countries (Woessmann, 2009 ).

Therefore, the school tracking often leads to the constitution of rather homogenous student groups, but according to school types with different curricula. This thus leads to classes in which the students have almost the same achievement levels. In other words, the school tracking leads to grouping based on the previous performance, with higher achieving students being separated from lower achieving students (Brunello, 2004 ). The effects of an increase in the number of school types on students’ academic performance and optimal tracking age are much discussed issues within scope of equality and efficiency in education (Brunello, 2004 ; Hanushek and Woessmann, 2006 ; Marks, 2006 ; Reichelt et al., 2019 ; Roemer, 1998 ; Woessmann, 2009 ; Zimmer, 2003 ).

Historically, source of differentiation via school tracking in education systems stemmed from the increase in demand of the labor markets towards vocationally skilled workers after industrial revolution (Bell, 1973 ; Davis and Moore, 1945 ). Since industrial revolution, labor market has remarkably changed, a great number of new occupations emerged and therefore, skills via general education have become insufficient. Education systems were restructured to gain students with demanded complex skills, resulted in a new type of schools called vocational track in addition to general or academic track (Benavot, 1983 ; Grubb, 1985 ). Increasing in the demand towards human resources with new skills have resulted in growing of vocational education and training (VET) to meet the manpower requirements (Trow, 1961 ). Therefore, the driving force of tendency towards VET in education systems is constituted by skill demands of the labor market caused by technological changes.

Before the establishment of VET institutions, vocational education was presented as apprenticeship programs with the support of craftsman and worker via artisanal and industrial trades union (Scott, 1914 ). On the one hand, including of VET to education systems led to weakening of union-controlled apprenticeship education; on the other hand, it led to raising of the disciplined manpower, which is appropriate for division of labor, empowering the national integration (Benavot, 1983 ).

It is also claimed that education tracking has a latent purpose of maintaining the social distance between social classes (Bowles and Gintis, 1976 ; Collins, 1979 ; Lucas, 1999 ; Marshall, 1950 ). Since the gap between well-educated elite class and less-educated lower classes was weakened after the expansion in education, it is expressed that educational tracking was structured to strengthen the gap (Bol and Van de Werfhorst, 2013a , 2013b ). Bourdieu and Passeron ( 1990 ) emphasized that education provides an important instrument to maintain the current social classes within social structure. In other words, educational stratification continues its existence and provides the reproduction of social classes through the school tracking. In this context, ratio of VET to general education is considered as measure of social stratification (Bertocchi and Spagat, 2004 ).

Although the tracking is applied to separate a room for VET, the tracking is discussed in terms of advantages, as well as disadvantages for VET and equality in education and opportunity. The discussions about the impact of tracking focus on the tracking age, the percentage of curriculum differentiation, and the number of tracking schools (Reichelt et al., 2019 ). An early tracking makes the student’s achievements much more dependent on their socioeconomic background (Marks, 2006 ; Reichelt et al., 2019 ). If the degree of curriculum differentiation is higher, and as such very specific to particular occupations, as for example in Germany, then youth unemployment is lower and the school-to-work time is shorter (Bol and Van de Werfhorst, 2013a , 2013b ; Hanushek et al., 2011 , 2017 ). However, graduates of VET have fewer chances for higher education and prestigious professions (Müller and Shavit, 1998 ). In addition, their mobility between occupations is also limited (Solga et al., 2014 ). Therefore, the tracking seems to provide a mechanism of social reproduction by diverting working-class students from higher education and prestigious professions, while at the same time locking them into a narrow occupational category (Shavit and Müller, 2000 ).

On the other hand, the vocational track does provide, or at least intends to provide, skill sets the labor market demands. If we leave this to machines, an automation procedure may spread in all the fields of everyday life and change working conditions and service experience, especially also in the light of evermore present artificial intelligence technologies (Perc et al., 2019 ). The new labor market dynamics demands some skill sets very fast by devaluating others, but also requiring ever new skills for new and emerging business lines and technologies (Acemoğlu and Restrepo, 2018 ). Therefore, beyond the school tracking and its effects, VET seems to be at a crossroads. In this context, some countries responded to this challenge by restructuring their VET systems in a more academic way, rather than vocationally, in particular by decreasing the degree of curriculum differentiation, allowing broader VET curricula, and by decreasing the number of training occupations (Solga et al., 2014 ).

At this point, the restructuring of VET systems thus requires taking into account both the school tracking and the new demands on VET. In this study, the effects of tracking are evaluated in the context of equality in education and opportunity. The impact of tracking on VET is also surveyed and suggestions for the restructuring of VET are presented in this context.

Tracking and equality

The degree of school tracking became a much discussed issue of educational systems all around the world in terms of its intended and unintended consequences (Bol and Van de Werfhorst, 2013a , 2013b ; Brunello, 2004 ; Hanushek and Woessmann, 2006 ; Marks, 2006 ; Pekkarinen et al., 2006 ; Reichelt et al., 2019 ; Roemer, 1998 ; Woessmann, 2009 ; Zimmer, 2003 ). What is the long-term impact of educational tracking on equality in terms of education and opportunities later in life? An important argument in favor of school tracking is that it allows the specialized curriculum for homogenous classes (i.e., VET) and presents the convenience for maximum learning. Thus, it creates an opportunity to decrease the concerns of teachers for classes with both fast learning- and slow learning students (Hanushek and Woessmann, 2006 ). In other words, it seems easier for student groups with the same abilities to focus on certain learning goals and educational progress in the tracked systems (Jacobs and Wolbers, 2018 ). However, it is also expressed as an opposing view that tracking leads to systematic disadvantages for lower performing students who are already studying in environments, which do not promote learning sufficiently. In addition, the tracking in early ages increases the noise effect of tracking (risk of students’ misclassification) (Brunello, 2004 ). When students are tracked earlier, students’ performance becomes more dependent on their social background (Marks, 2006 ; Reichelt et al., 2019 ). Therefore, earlier tracking increases the risk of misallocating individuals to the tracks (Brunello, 2004 ).

When nonlinear peer effects are considered, heterogeneous classes lead to increase in efficiency, providing that low-performing students have better opportunities to increase their performance via more effective group discussions and motivation based on interactions while high-performing students do not have any disadvantage (Hanushek and Woessmann, 2006 ). Thus, the tracking that leads to homogeneity may deprive of low-performing students from these opportunities (Zimmer, 2003 ). Criticism about tracking in early ages mainly focus on systematic disadvantageous position of low-performing students in these conditions (Woessmann, 2009 ). Another common concern about the early tracking is an increase in inequality of opportunities because of that the lower ability students may be clustered in schools with lower level dominated by the family effects (Hanushek, 2019 ). Since they are clustered in lower tracks, they have fewer chance for attending university and finding more prestigious occupations (Müller and Shavit, 1998 ).

In this context, researchers studied the impact of the tracking age on inequality based on international large-scale student assessment data such as PISA, TIMSS, and PIRLS. Hanushek and Woessman ( 2006 ) studied the effects of early tracking on the students’ performance and distributions based on the PISA, TIMSS, and PIRLS data and found that it increases the inequality in education. They compared the countries with different tracking ages and showed that countries with highest inequality between primary schools and high schools are the ones, in which the tracking is implemented in early ages. They also found that countries with lowest inequality between primary schools and high schools are the ones, in which the tracking is implemented later than the PISA sample age. In other words, educational inequality increases systematically in countries, which implement early tracking. It is also seen that Germany is the country with the highest increase in inequality, where also the tracking is implemented at a quite an early age (Woessmann, 2009 ).

On the other hand, it is known that family background is one of the most effective factors on students’ academic achievement in both national and international studies (Woessmann, 2009 ). When it is considered that students’ family- and socioeconomic backgrounds have significant impact on their academic achievement before the tracking, the inequality is still in existence even in primary education level. Therefore, the tracking that depends on students’ academic performance even worsens the disadvantageous position of low-performing students’ and systematically deepens the inequality. This inequality is generally named as an inequality of opportunity (Betts and Roemer, 2007 ; Roemer, 1998 ; Schuetz et al., 2008 ; Woessmann, 2009 ).

Schuetz et al. ( 2008 ) investigated the dependence of students’ performance on family backgrounds and if there is systematically relationship with education policies of diverse countries, and found that inequality in opportunity decreases remarkably when the tracking age is delayed. In other words, when students are tracked earlier, students’ performance become more dependent on their family background. Woessmann et al. ( 2009 ) focused on the relationship between the tracking and the equality in opportunity and showed that difference of students’ performances from diverse socioeconomic backgrounds is quite large in countries with early tracking. Specific to Germany, the equality in opportunity for students from disadvantageous backgrounds is comparatively higher in states with less school types (Woessmann, 2009 ). Bol and Van de Werfhorst ( 2013a , 2013b ) studied the effects of tracking on educational functions and showed that the effect of socioeconomic background on science test scores increases and equality in opportunity decreases if level of tracking in education system is increased.

Recent studies focus on the role of education in the context of social reproduction through the flow from social origin to destination, firstly expressed by Bourdieu ( 1973 ) (Bernardi and Ballarino, 2016 ; Breen and Jonsson, 2005 ; Breen, 2010 ; Reichelt et al., 2019 ). Since the degree of economic status’ transferability within families through generations is considered as an important indicator, both intergenerational income mobility and intergenerational income correlations are investigated in these studies. For example, Dustmann ( 2004 ) showed that high intergenerational correlation in Germany is related with the early tracking. Meghir and Palme ( 2005 ) studied the impact of the comprehensive school reform in Sweden in 1950s’ on the income of students from less-educated families, and found that it led to an increase in the intergenerational income mobility. Pekkarinen et al. ( 2006 ) investigated the impact of Finland’s comprehensive schooling reform in 1970s’ on intergenerational income mobility and found that the reform led to a 20% decrease in intergenerational income correlation. Two fundamental characteristics of Finland comprehensive schooling reform are the postponement of tracking age from 11 to 16, and presenting the same curriculum, which is intensified with academic content, to all students until the age of 16. It is considered that both characteristics played important roles to achieve that result. Postponement of tracking age leads to decrease the effect of family background on educational attainment and increases the education mobility, and thus, decreases the intergenerational income elasticity. Besides, academically intensified curriculum of the comprehensive education has a positive effect on lifetime income levels of students from lowincome families and thus, it leads to decrease in intergenerational income correlation (Pekkarinen et al., 2006 ).

Reichelt et al. ( 2019 ) studied the effect of school tracking on social reproduction via three indicators, i.e., the tracking age, the percentage of curriculum differentiation of tracked schools, and the number of tracking schools. Results showed that a decrease in the tracking age and an increase in the percentage of curriculum differentiation are related with greater educational inheritance and the number of tracking schools is related with direct effect of social origin. On one hand, the relationship between family education level and occupational status is positive and significant in all included countries, on the other hand, direct effects of educational inheritance and social origin become stronger if the tracking level is increased (Reichelt et al., 2019 ).

In addition, the tracking is also effective on the civic behaviors of citizens. Hyland ( 2006 ) showed that democratic attitudes are balanced in higher levels when the student composition in classes becomes more heterogeneous. Janmaat and Mons ( 2011 ) found that variation of civic competences increases in countries where the tracking exists. Bol and Van de Werfhorst ( 2013a , 2013b ) studied the effects of tracking on educational functions and found that possibility of students to become active citizens decreases when the degree of tracking is increased. Therefore, the tracking also leads to downshift of active citizenship.

Tracking and vocational education

Apart from facilitating inequalities and unfair chances in life; if applied at too early stages of education, the tracking is supposed to have a favorable impact on vocational education in that it provides the workforce the market demands. However, is this truly the case? As we will argue in what follows, the tracking seems to have a negative impact on the quality of vocational education, because lower achieving students separated for vocational track cannot achieve the desired competencies that are increasingly needed to keep pace with technological innovations and high-end services.

Educational systems optimize the skills of students and prepare them for the labor market (Bol and Van de Werfhorst, 2013a , 2013b ). The tracked students are often trained to gain skills for a working life. However, the level of vocational orientation may show remarkable alterations between countries. For instance, VET programs are generally presented in a number of broader fields and applied school-based way in most countries while, in some countries, VET is presented as dual system model, in which education and on-work trainings in companies are combined and specific skills towards workplaces are intensified (Bol and Van de Werfhorst, 2013a ; Breen, 2005 ). Therefore, types and processes of VET differ across countries (Hanushek et al., 2011 ; Müller and Gangl, 2003 ; Müller and Shavit, 1998 ). In work-based VET systems, students gain specific skills towards workplaces while students gain more general skills in school-based VET systems (Bol and Van de Werfhorst, 2013a ).

School tracking seems to have some advantages for the vocational track. It is found that the tracking has positive effects on the labor market allocation for VET graduates (Bol and Van de Werfhorst, 2013b ). Results of international studies, which focus on students’ academic achievement and literacy levels, are widely used to evaluate the readiness of graduates towards working life. In this context, a comparative analysis of general and VET programs is crucial for observing the indirect effects of the tracking. For instance, Hanushek et al. ( 2011 , 2017 ) compared the employment status and salaries of VET and general education graduates based on the results from the OECD’s International Adult Literacy Survey (IALS), allowing direct monitoring of cognitive skills in different age groups and school types. They found that VET graduates have the advantage in employability and income levels between 16- and 26-year-old. Bol and Van de Werfhorst ( 2013b ) also showed that in countries where VET-oriented tracking is implemented, youth unemployment is lower and the duration of the school-to-work transition is shorter. In this context, the German dual VET system has received praise for decades because of its beneficence for school-to-work transitions. Since it is actively embedded in the labor market structure and provides highly occupation-specific skills, the youth unemployment rate in Germany is low. For example, in 2012, 66% of the apprenticeship graduates have been employed by the firm in which they were trained (Solga et al., 2014 ).

On the other hand, Hanushek et al. ( 2017 ) also showed that graduates of general education take the advantage in employability and income levels as age increases. In other words, VET graduates are employed at higher rates immediately after graduation with the support of workplace training, but vocational skills become insufficient against the skills requested by the labor market in later stages as age increases lead to a decrease in both employability and income level (Hanushek et al., 2017 ). This change is especially much more apparent in countries such as Germany where both the work-based vocational education model is predominantly applied and the tracking is implemented in quite early ages (Hanushek et al., 2011 ). Therefore, we may suggest that the early tracking does not bring any advantage for VET graduates in terms of life-long employability and income levels. Ironically, although the tracking is implemented for its own use, VET seems to be an education type, which is affected negatively by the tracking to great extent. In most OECD countries, VET is perceived as a low-status education type and it is mostly evaluated as a second- or third-choice (CEDEFOP 2018 ; Chong, 2014 ; Ozer, 2018 ; Ozer, 2019a ; Ozer, 2019b ; Sahlberg, 2007 ). VET also suffers from high school drop-out and absenteeism ratios (Abusland, 2014 ; CEDEFOP, 2018 ; EQAVET, 2015 ; Vantuch and Jelinkova, 2013 ; Waltzer and Bire, 2014 ). Generally, children of lower socioeconomic families or immigrant families are clustered in VET programs (Ozer et al., 2011 ; Ozer, 2019b ).

The value of skills gained in VET decreases faster in response to the rapid technological changes, leading to those vocational skills becoming insufficient and indeed not needed by the labor market as age increases (Hanushek et al. 2017 ). Since the skills of VET graduates become useless due to the dynamic structure of the labor market, and this especially so in fast growing economies, this also leads to increased investment costs of life-long learning at later stages (Hanushek, 2012 ). Moreover, since skills attained in general education facilitate adapting to technological transformation, this allows general education graduates to have better long-term employment opportunities. In other words, although general education has no immediate connection with a particular occupation, it provides more general and versatile skills that can be used to learn different occupations later in life (Brunello, 2004 ).

The Matthew effect

Although not featuring prominently in the existing literature on educational tracking, we nevertheless feel that the Matthew effect is crucial for understanding, and even for actually explaining, many of the long-term negative consequences of tracking (Perc, 2014 ). The Matthew effect posits that success breeds success, or that rich get richer. It has also been referred to as cumulative advantage, and as preferential attachment, all describing the fact that advantage tends to beget further advantage. In the realm of education, it simply means that those students who start well will finish even better in comparison to those who start poorly. Evidently, if educational tracking is applied early on, those who are initially downgraded, or perform poorly due to other socioeconomic factors, have an inherent disadvantage that gets more and more difficult to remedy as time goes by.

The Matthew effect was first made popular by the sociologist Robert K. Merton ( 1968 ), who took inspiration from the Gospel of St. Matthew, where it says “For to all those who have, more will be given”. Merton then used the phrase the Matthew effect to explain the discrepancies in recognition received by eminent scientists and unknown researchers for much the same work. However, already a few years earlier physicist Derek J. de Solla Price ( 1965 ) observed a similar phenomenon when studying the network of citations between scientific papers, only that he used the phrase cumulative advantage for the description. The concept today is in use to describe the general pattern of self-reinforcing inequality related to economic wealth, political power, prestige, knowledge, and in fact education (Rigney, 2013 ). The Matthew effect also contributes to a number of other phenomena in the social sciences that may be broadly characterized as social spirals. Examples include inflationary spirals, spiraling unemployment, and spiraling debt. These spirals exemplify positive feedback loops, in which processes feed upon themselves in such a way as to cause nonlinear patterns of growth, as illustrated in Fig. 1 .

We start on the left hand side with three barely visible circles. The blue circle has diameter 5, light-blue circle has diameter 4, and the cyan circle has diameter 3 (at this point, the colors are not distinguishable because the sizes of these circles are too small). If we assume the growth is proportional with the size, during each time step the circles become larger by a factor equivalent to their current diameter. After the first time step, shown in the middle, this gives us circles of sizes 25, 16, and 9, respectively. Continuing at the same rate, after the second time step shown on the right hand side, we have sizes 625, 256, and 81. Clearly, such a procedure quickly spirals out of easily imaginable bounds. In this example, the diameter of the circles can represent anything, from the amount of money one owns to literacy during formative years. The key message is that even tiny differences at early stages grow out of proportions fast if proper precautions are not taken. Educational tracking can easily contribute to exacerbating very small differences in early school performance to life-changing proportions.

An insightful synthesis titled "Matthew effects in reading: Some consequences of individual differences in the acquisition of literacy" is due to Stanovich ( 2008 ), where a framework is presented for conceptualizing the development of individual differences in reading ability, with special emphasis on the concepts of reciprocal relationships. Foremost, it is explained how these mechanisms operate to create the rich-get-richer and the poor-get-poorer patterns of reading achievement, and the framework is used to explicate some persisting problems in the literature on reading disability and to conceptualize remediation efforts in reading. It is emphasized that due to the Matthew effect, early deficiencies in literacy may bread life-long problems in learning new skills, and falling behind during formative primary school years may create disadvantages that could be difficult to compensate all the way to adulthood (Stanovich, 2008 ).

According to the Matthew effect, and based on troves of folktales, and based also on data on the social capital hypothesis and social networks, being born into poverty greatly increases the probability of remaining poor, and each further disadvantage makes it increasingly difficult to escape the economic undertow. The same applies to education, and educational tracking to less demanding programs based on poor early performance essentially shuts many doors that would remain open were it not for the selection. Accordingly, in the light of these information and statistical data on the performance of educational tracking, it is at the very least strongly advisable that tracking be postponed to much later in life, and then also with ample options to revert to other tracks as needed.

Although the school tracking may bring short-term advantages for VET graduates, our review shows that it also increases the inequality in education and opportunity. Negative effects of tracking become especially obvious when the tracking is implemented in early ages, and this simply because of the fact that the impact of the socioeconomic background is then often too decisive and determinant. So who are those that get the best of tracking in early ages? Hanushek and Woessman ( 2006 ) found that both low-performing students and high-performing students suffer from the early tracking, and no one gets more at the expense of the others. On the other hand, students from low-socioeconomic backgrounds perform higher and students from high socioeconomic backgrounds perform at similar levels if the tracking age is delayed (Woessmann, 2009 ; Woessmann et al., 2009 ).

The school tracking is also related with inequality in the labor market in the long-term. It is shown that there is a close relationship between income inequality and inequality in educational performance (Woessmann, 2009 ). Students from disadvantageous backgrounds are mostly clustered in lower levels and thus, they have lower chances to get into university and prestigious occupations (Müller and Shavit, 1998 ; Reichelt et al., 2019 ). Nickell ( 2004 ) showed that majority of international differences in income inequality can be dedicated to international differences of inequality in educational achievement, which can be evaluated via international academic achievement and literacy studies. Retarding of the tracking age also decreases the inequalities, which can be occurred in labor market in a long-term (Meghir and Palme, 2005 ; Pekkarinen et al., 2006 ; Woessmann, 2009 ).

Reforms in education systems to minimize the effect of tracking focus on the delaying of the tracking age, maintaining the comprehensive education and presenting same curriculum to all students until the tracking age (Meghir and Palme, 2005 ; Pekkarinen et al., 2006 ). By this way, it is intended to decrease both inequality in education and inequality in opportunity. Consequently, under the assumption that socioeconomic differences and disadvantageous backgrounds will continue their existence, it is essential that education systems need to focus on the comprehensive education rather than tracking as much as possible in order to ensure equality in education and opportunity.

On the other hand, since the school tracking will allow the implementing of vocational curriculums in schools, delaying of school tracking to later ages will increase the equality in opportunity without any cost totally. At this juncture, the percentage of curriculum differentiation is critical. Especially, reducing the percentage as much as possible has a potential to minimize the negative effects since the current situation of labor market gives an opportunity for this transformation in VET.

Nowadays, automation has become widespread and supported by artificial intelligence technologies all over the world, thus challenging and changing the labor market and its dynamics (Perc et al., 2019 ). The labor market regulates skill demands more dynamically through devaluating some skills while bringing new skills to the fore. This transformation, which is dominated by automation and artificial intelligence, has led on one hand to a negative effect on employment (displacement effect) but on the other hand to new skills for new business lines (Acemoğlu and Restrepo, 2018 ). If the VET does not supply enough skills dynamically in response to dynamic change in skills demand, there may be shortages in one sector and oversupply in other sectors, ultimately resulting in a mismatch in the labor market (Johansen and Gatelli, 2012 ). This skill mismatch eventually increases the risk in employability and/or leading to an increased employment for low-skilled jobs. When the training of VET students is closely linked to occupation-specific skills, as in Germany, it results in a decrease in the occupational mobility of the graduates on one hand, and an increase in the risk of unemployment or employment only in low-skilled jobs in the long-term on the other hand. In other words, although the high degree of occupational specificity in Germany facilitates the school-to-work transition, it limits horizontal and upward occupational mobility. Therefore, those who have to leave their initial occupation for some reason are exposed to higher risks of unemployment and downward mobility such as semi- or low-skilled jobs (Solga et al., 2014 ). In addition, in Germany, since the VET curriculum is revised in a consensual manner by representatives of the federal government, state governments, chambers, trade unions and experts, it takes many years to do so (Solga et al., 2014 ). Therefore, the employability advantage of VET graduates in Germany decreases as age increases (Hanushek et al., 2017 ). On the other hand, since the dual VET system includes the students from early tracking, it has also been criticized for channeling working-class children into the VET track and diverting them from entering higher education (Mayer et al., 2007 ; Powell and Solga, 2011 ; Shavit and Müller, 2000 ; Solga et al., 2014 ).

In countries with a stronger emphasis on vocational education in the form of a dual system, since students are prepared for a more specific job in the occupational structure, young employees spent more time in the same job (Bol and Van de Werfhorst, 2013b ). New conditions make the restructuring of VET inevitable in line with the new dynamics of the labor market. One clear implication of the new market is the relative demand shift toward more general and versatile skills (Brunello, 2004 ). In addition, flexible mobility between professions and the strengthening of general and versatile skills must likewise be an integral characteristic of the new VET structure (Brunello, 2004 ; Sahlberg, 2007 ). Since the general track appreciates more general and versatile skills, graduates of general education tracks get ahead of the VET graduates in employability advantage as age increases (Hanushek et al., 2017 ). Therefore, Germany for example, seeks an efficient way to restructure its VET system. In this context, a new hybrid form of a dual system has emerged recently as a joint initiative with large firms, which allows both a vocational certificate and a bachelor’s degree (Solga et al., 2014 ). Furthermore, in order to prevent overspecialization in VET and facilitate the mobility especially in later work life, a broader definition of occupations, a lower degree of occupational specificity, and a lower number of training occupations have all been proposed (Müller and Shavit, 1998 ). Accordingly, Denmark also revised its dual VET system by reducing the duration of workplace training, by decreasing the degree of curriculum standardization, by allowing more broad-based curricula, and by decreasing the number of training occupations (Solga et al., 2014 ).

Countries that consider VET as a career path where academic skills can be gained only to a limited extent may push themselves into a disadvantageous position in the long-term development process (Hanushek, 2012 ). At the moment, the skills gained through VET are not alternatives for general skills; on the contrary, they become an integral part of general skills. In this context, restructuring in VET requires both simplifying VET fields and branches and increasing the share of general and versatile skills to increase the life-long employability in response to the new labor market (Ozer, 2019b ). Therefore, in order to meet the demands of new labor market, restructuring of VET, in such a way that will gain students to broader skills instead of specific skills peculiar to certain occupations, allow students for flexible transitions between occupations and motivate them to gain general skills, will empower the resilience of VET graduates and increase the life-long employability of them.

After the industrial revolution, the ratio of VET in general education increased. However, this increase came to a top in 1950s’ while later this ratio began to decrease (Benavot, 1983 ). When the ratio of VET to general education is considered as an indicator of social stratification (Bertocchi and Spagat, 2004 ), this reduction means a relative decrease in the social stratification. The later tracking also decreases the inequalities in the labor market, especially in the long-term (Meghir and Palme, 2005 ; Pekkarinen et al., 2006 ; Woessmann, 2009 ). Besides, new transformation of VET, demanded by the labor market, will decrease the percentage of curriculum differentiation in VET. Both characteristics of this new trend will eventually decrease the inequality in education and opportunities. The rise of VET after the industrial revolution is explicitly related with supply for the demands of the labor market and the maintenance of the status’ of social classes in a society in a latent complex manner. The restructuring of VET, supported by new characteristics of the labor market, is thus related not only with the changing needs of this market, but also with the increased equality in education and job opportunities later in life.

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Acknowledgements

M.P. was supported by the Slovenian Research Agency (Grant Nos. J4-9302, J1-9112, and P1-0403).

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Mahmut Ozer

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Department of Medical Research, China Medical University Hospital, China Medical University, Taichung 404, Taiwan

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Ozer, M., Perc, M. Dreams and realities of school tracking and vocational education. Palgrave Commun 6 , 34 (2020). https://doi.org/10.1057/s41599-020-0409-4

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The Upside of Academic Tracking

New evidence suggests that black and Latino students thrive in honors class.

Tracking, the practice of putting a small group of higher achieving students into separate advanced or honors classes, isn’t popular with progressive educators. Previous research has pointed out that it exacerbates inequality in our schools because higher income and white or Asian kids are more likely to get tracked into the elite classrooms. Students who aren’t chosen can become demoralized, or the curriculum in the average class can get too watered down. Great teachers and extra resources get steered to these honors programs, leaving the kids who need the most help with less. Researchers have sometimes found that lower-achieving kids are worse off in schools that track.

Now two fresh studies, both published in March 2016, make a compelling case for continuing to cream off the top students and teach them in separate classrooms. One, from the Brookings Institution, suggests that the United States won’t produce as many students, including blacks and Hispanics, who can master higher mathematics if schools don’t begin preparing them separately, starting in eighth grade. The second one, from two economists, finds that tracking can close the achievement gaps both between high-IQ blacks and whites and between high-IQ Hispanics and whites.

The Brookings researcher, Tom Loveless, found that states that track more students into different ability levels in eighth-grade math wind up with more students scoring better on Advanced Placement exams, typically taken by top students during the senior year of high school. States where tracking isn’t practiced as much had fewer students hitting a passing score of 3 or higher on AP tests. *

“We’re talking about a very rarefied group of high-achieving kids who are taking the toughest courses and the toughest tests,” said Loveless, the author of the study . “My point is that they don’t just get there out of thin air. You need to cultivate talent over time in mathematics.”

“I draw the analogy to sports,” he added. “When we hear about the high school starting quarterback who’s a great star, he started playing football when he was eight. We’re not shocked to hear that these kids were identified very young, and that they were offered completely differentiated opportunities to cultivate their talent.”

Math isn’t football, of course, and schools strive to help all children excel at math. But Loveless’s research raises an age-old question in education of whether excellence is sacrificed by well-intended efforts to promote equity.

Tracking in eighth-grade math—steering some to algebra and most others to another year of general math—remains popular across the United States. It’s also a critical decision in a student’s life. Kids who don’t study algebra in eighth grade proceed on a path that effectively shuts them off from calculus and advanced science classes. On average, Loveless found that states tracked about three-quarters of eighth-graders in math, with Arkansas tracking the least (50 percent) and Nevada the most (97 percent). (The data came from surveys of school principals conducted by the National Assessment for Educational Progress, or NAEP, in 2009).

Four years later, in 2013, roughly around the time that these eighth-graders would have been eligible to take an AP exam, Loveless found that states with more tracking had more passing scores. For example, Utah tracks 89 percent of its students in eighth-grade math, and 70 percent of AP test takers in Utah scored a 3 or higher. In Texas, only 57 percent of students are tracked, and only 52 percent of the state’s AP test takers passed.

Loveless was concerned that states with more poverty might be producing fewer high AP scores, and that his results might be unrelated to tracking. But he controlled for poverty, and still found that a tight relationship between tracking and AP scores. The relationship also held true for minority groups. Higher percentages of black and Hispanic students scored well on the AP test in states where there was more tracking.

The other study found big benefits for high-achieving minority students who were tracked into “gifted” classes. It’s a working paper at the National Bureau of Economic Research, written by David Card at the University of California, Berkeley, and Laura Giuliano, at the University of Miami. They studied an unnamed school district, described as one of the largest in the country, where elementary schools were required to establish gifted classes even if there was only one child who scored high enough to qualify. So the rest of the seats would be filled by high-achieving kids in that school who had missed the “giftedness” cutoff. Because the schools are quite segregated in this district, many high-achieving black and Hispanic children ended up in these elite classrooms.

The economists found that these high-achieving black and Hispanic students flourished. Their academic gains were equivalent to what researchers are finding at the best charter schools, and the benefits persisted through at least sixth grade.

As importantly, there was no trade-off between fostering excellence and promoting equity in this case: the researchers didn’t find negative consequences for students who weren’t selected or narrowly missed the cutoff to enter the gifted classrooms.

Interestingly, the benefits to joining these classrooms were the biggest for minority students. The researchers didn’t find great gains for high-achieving white students. Whites of similar academic abilities did about as well in the regular class as they did in the gifted class.

“We show that minority students have lower achievement scores than white students with the same cognitive ability, and that placement in a [gifted] class effectively closes this minority underachievement gap,” the authors wrote.

Superior teaching in the gifted classes couldn’t explain this, they said. So Card and Giuliano hypothesize that two other things are going on: teacher expectations and peer pressure. In regular classrooms, teachers may be overlooking higher-ability minority students and not pushing them as hard as they could be pushed. But in gifted classrooms, teachers are expecting excellence from everyone. Secondly, the researchers wonder if smart minority students are particularly susceptible to peer pressure in regular classrooms, where it’s not “cool” to be smart. In the gifted classrooms, classmates may be more supportive of working hard and getting A’s.

The big argument against tracking is that black and Hispanic students are penalized by it. But perhaps tracking is what is needed to get more blacks and Hispanics into the elite ranks of top scientists and mathematicians.

This post appears courtesy of The Hechinger Report .

* The original version of this story published by ​ The Hechinger Report​ and The Atlantic incorrectly characterized the AP test scores that were compared in the Brookings study. We regret the error.

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FACTORS THAT AFFECTS GRADE 10 STUDENTS IN CHOOSING ACADEMIC TRACK IN SENIOR HIGH SCHOOL

Factors Affecting Grade 10 Students from Maligaya High School in choosing Academic Track for Senior High School for the School Year 2019-2020 In partial fulfillment for the subject Practical Research 1 To be presented for the faculty members of ACCESS COMPUTER AND TECHNICAL COLLEGES - Lagro Campus By: (List the name of each member alphabetically)

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With the changes that are needed to be faced by our country in terms of educational curriculum, the researchers have made a move to pursue this study. In our study, we concluded four (4) major factors which was the basis of this study, Parental Influence, Aptitude, Interests and Environmental Factors. This study aims to find out the significant differences between the career choice factors and the gender of our respondents. The research was conducted at the University of the Immaculate Conception and its respondents were selected Grade 10 students, ranging from 20-23 per section. It was performed using the descriptive survey method, thus, the researchers formulated a questionnaire based on the four (4) different indicators, with six (6) statements each. The questionnaires were distributed in 8 sections, with 172 respondents all in all which was verified through the Slovin’s formula. The researchers then encoded the data to be able to get the mean scores, as well as, the p-value or the significant difference. It was then formulated by the SPSS, and obtained a p-value of 0.144. Therefore, it was implied that there was a significant difference between the career choices of grade 10 students with their gender. The proponents’ decision was to accept the alternative hypothesis and reject the null hypothesis. There are diverse and several factors which can also affect the career choice of an individual. For the improvement of further studies, the researchers highly recommend that there should be other factors that will be looked upon since career choice is essential in one’s future way of life.

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This study aims to determine the factors that affect the senior high school track preferences of the Grade 9 students of Don Bosco Technology Center of academic year 2014-2014. This study utilizes descriptive method of research to determine the factors. It would see if dependent variables relating to personality, family/relatives, interests and job opportunities were significant factors influencing the track preferences of the respondents. The descriptive research used quantitative methods to assess the feedback from the respondents

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There are many influences that affect the preferences of grade 10 students in choosing a track to proceed to senior high school. Likewise, this study aims to identify influence of preference of a Senior High School track that is commonly encountered by the Grade 10 students in terms of Gender, Socio-Economic Status, Average academic grades, nature of parent’s occupation; and, strand and the level of influence of the respondent to be associated with preferences in choosing a track in senior high school in terms of family influence-decision; peer influence; financial condition; and employability. The research tool was a survey questionnaire. The questionnaire is composed of respondent’s profile and 10 statements to be rated. The factors fairly influence preferences of the senior high school. In terms of gender, male students consider their socio-economic status and their parent’s occupation as factors in choosing their track in Senior High School while female students consider thei...

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The study highlighted the transition rate of Grade10-Junior High School (JHS) completers to Grade 11-Senior High School (SHS) enrolment, and students’ track preferences. The study utilized quanti-quali approach. Quantitative data were collected and analyze from school’s records/forms. Qualitative data were based from direct responses of the respondents. It employed descriptive-evaluative type of research design and applied purposive sampling to 41 students to obtain descriptions out from the results of evaluation. The high percentage distribution of “Balik-aral” students, classified as parent-students, contributed an increase of Grade 11 enrolment. The career goals of SHS program, College and Business/Commerce, encouraged the community to patronize to study the track offered by the school. Students’ mastery level did not suffice the passing standard in education that the SHS track offered by the school was not relevant to the preferences of students. Thus, the school may assess or evaluate school’s program and curriculum instruction suited to learners’ learning style to prepare them according to their level of preferences. KEYWORDS: Students’ Preferences, SHSTrack, Curriculum, Transition Rate

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This study aims to determine the factors that affect the senior high school track preferences of the Grade 9 students of Don Bosco Technology Center of academic year 2014-2014. This study utilizes descriptive method of research to determine the factors. It would see if dependent variables relating to personality, family/relatives, interests and job opportunities were significant factors influencing the track preferences of the respondents. The descriptive research used quantitative methods to assess the feedback from the respondents. Scale/questionnaire is given to the respondents to conduct the study personally and is collected after to gather all the results. Most of the literature gathered talks about the factors that affect career preferences/choices, namely personality, family, interests and job opportunities, which would specialized in senior high school of the K-12 curriculum.

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Longitudinal Academic Tracks

Our Longitudinal Academic Tracks allow students to explore various areas of interest in conjunction with the four-year medical school curriculum.  All tracks are faculty-run, scholarly experiences for medical students interested in developing attitudes and skills for self-directed, lifelong learning and career development.

The goals of the Longitudinal Academic Tracks are to promote intellectual curiosity, appreciation of scholarly inquiry, inter-professional collaboration, and cura personalis .

Longitudinal Academic Tracks currently offered in the School of Medicine:

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Interested students may apply during first year of medical school. Students who successfully complete a track by meeting all track requirements and in good academic standing, graduate from the medical school with distinction.

Students may apply to up to two academic tracks. The track application becomes available to students in late Fall of their first year.

The Longitudinal Academic Tracks are extracurricular opportunities that enhance the core curriculum and allow students to build upon a specific interest in medicine over the course of the four-year undergraduate medical education curriculum. In effort to ensure prioritization of core curricular elements and support workload balance, students must be in good academic standing (as outlined below) to participate in a Track.

Any student below passing in two longitudinal courses, or below passing in one longitudinal course and below 70% in two or more modules, will NOT be eligible to participate in an extracurricular track. Longitudinal courses that are less than 10% complete at the end of Block 2 are not considered in the determination of good academic standing for track participation eligibility.

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ART seminars introduce research concepts, useful in planning a mentored summer or 'year out' research project. Taught in small lunchtime seminars (light lunch included) occur throughout the medical school year. Modules consisting of two to three seminars each cover such topics as mentoring and being mentored, formulating a research question, measurements, data gathering and presentation, obtaining research funding, and writing for publication. The goal is to help students think about science as they plan for a mentored research experience, which is typically taken after the second or third years of medical school.

The ART program administrator is Jene Dupra .

The faculty contact is Candace Gildner .

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Baqir Kedwai Project: Determination of the Natural History of Aortic Dissection Tissue Mechanics Using Non-invasive Elastography Mentor: Doran Mix, M.D.

Daniel Lehane Project: Patient-Reported Outcomes for Ruptured Abdominal Aortic Aneurysm Mentor: Karina Newhall, M.D., M.S.

Tori Valachovic Project: Assessing Reproductive Health Care and Perspectives amongst Birthing People with Epilepsy Mentor: Sarah Betstadt, M.D., M.P.H.

Marissa LoCastro Project: Engagement in Advanced Care Planning Among Older Patients with Acute Myeloid Leukemia and Myelodysplastic Syndrome Mentor: Dr. Melissa (Kah Poh) Loh

Caroline Maretz Project: Infectious Keratitis: Epidemiology, Bacterial Virulence and Antimicrobial Resistance Mentor: Dr. Rachel Wozniak

Ching-Wei Pan Project: Molecular Mechanism of GATA1 Mutation Causing Transient Abnormal Myelopoiesis in Down Syndrome Mentor: Dr. Laurie Steiner

Mariah Erlick Project: Assessing Value in the Surgical Episode of Care: A Conceptual Model and Implementation Framework Aligning Patient and Provider Values in Gastrointestinal Oncologic Surgery Mentor: Dr. Larissa Temple

Eleanor Pope Project: Longitudinal Real World Changes in Skin Microbial Ecology in AD Patients Mentor: Dr. Lisa Beck

Kevin Vo Project: Targeting Beta-II Spectrin to Improve CAR-T Cell Therapy Mentor: Dr. Minsoo Kim

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Ron Menorca Project Title: Determining EPO's role in accelerating functional recovery of meme in a mouse chronic neuropathy model Mentor: John Elfar, M.D.

Doran Mix Project Title: Computer Assisted Surgery Tool for Hemodialysis Fistulas (CAST-HDF) Mentor: Ankur Chandra, M.D.

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Gregory Ouellet Project Title: Effect of Sex Hormones on Clinical Course of Patients with Congenital Long QT Syndrome Mentor: Arthur Moss, M.D.

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Research Article

Academic Tracker: Software for tracking and reporting publications associated with authors and grants

Roles Methodology, Software, Validation, Writing – original draft, Writing – review & editing

Affiliation Superfund Research Center, University of Kentucky, Lexington, KY, United States of America

Roles Conceptualization, Methodology, Writing – review & editing

Affiliations Superfund Research Center, University of Kentucky, Lexington, KY, United States of America, Department of Computer Science (Data Science Program), University of Kentucky, Lexington, KY, United States of America, Markey Cancer Center, University of Kentucky, Lexington, KY, United States of America

Roles Conceptualization, Funding acquisition, Methodology, Project administration, Supervision, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliations Superfund Research Center, University of Kentucky, Lexington, KY, United States of America, Markey Cancer Center, University of Kentucky, Lexington, KY, United States of America, Department of Molecular and Cellular Biochemistry, University of Kentucky, Lexington, KY, United States of America, Institute for Biomedical Informatics, University of Kentucky, Lexington, KY, United States of America, Center for Clinical and Translational Science, University of Kentucky, Lexington, KY, United States of America

• P. Travis Thompson, 
• Christian D. Powell, 
• Hunter N. B. Moseley

• Published: November 18, 2022
• https://doi.org/10.1371/journal.pone.0277834
• Peer Review
• Reader Comments

In recent years, United States federal funding agencies, including the National Institutes of Health (NIH) and the National Science Foundation (NSF), have implemented public access policies to make research supported by funding from these federal agencies freely available to the public. Enforcement is primarily through annual and final reports submitted to these funding agencies, where all peer-reviewed publications must be registered through the appropriate mechanism as required by the specific federal funding agency. Unreported and/or incorrectly reported papers can result in delayed acceptance of annual and final reports and even funding delays for current and new research grants. So, it’s important to make sure every peer-reviewed publication is reported properly and in a timely manner. For large collaborative research efforts, the tracking and proper registration of peer-reviewed publications along with generation of accurate annual and final reports can create a large administrative burden. With large collaborative teams, it is easy for these administrative tasks to be overlooked, forgotten, or lost in the shuffle. In order to help with this reporting burden, we have developed the Academic Tracker software package, implemented in the Python 3 programming language and supporting Linux, Windows, and Mac operating systems. Academic Tracker helps with publication tracking and reporting by comprehensively searching major peer-reviewed publication tracking web portals, including PubMed, Crossref, ORCID, and Google Scholar, given a list of authors. Academic Tracker provides highly customizable reporting templates so information about the resulting publications is easily transformed into appropriate formats for tracking and reporting purposes. The source code and extensive documentation is hosted on GitHub ( https://moseleybioinformaticslab.github.io/academic_tracker/ ) and is also available on the Python Package Index ( https://pypi.org/project/academic_tracker ) for easy installation.

Citation: Thompson PT, Powell CD, Moseley HNB (2022) Academic Tracker: Software for tracking and reporting publications associated with authors and grants. PLoS ONE 17(11): e0277834. https://doi.org/10.1371/journal.pone.0277834

Editor: Yuji Zhang, University of Maryland Baltimore, UNITED STATES

Received: April 1, 2022; Accepted: November 3, 2022; Published: November 18, 2022

Copyright: © 2022 Thompson et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are located at: https://doi.org/10.6084/m9.figshare.19412165 .

Funding: This work was supported in part by grants NSF 2020026 (PI Moseley - HNBM), NIH P42 ES007380 (PI Pennell; co-I HNBM) via the Data Management and Analysis Core (DMAC), and NIH U54 TR001998-05A1 (PI Kern; co-I HNBM). There was no additional external funding received for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Since 2008, the United States government has passed laws and issued directives to promote public access to peer-reviewed publications resulting from federal funding. These requirements started with Division G, Title II Section 218 of the Public Law (PL) 110–161 also known as the Consolidated Appropriations Act of 2008 [ 1 ], which directed the National Institutes for Health (NIH) to require all peer-reviewed publications supported by NIH funds to be electronically submitted to PubMed [ 2 ] within 12 months of the official date of publication [ 3 ]. Second in 2013, the White House Office of Science & Technology Policy (OSTP) mandated that all federal agencies with research and development budgets over $100 million to develop public access plans for research publications and data resulting from grants provided by these federal agencies [ 4 ]. Shortly thereafter in 2014, the US Congress passed the FY 2014 Omnibus Appropriations Act [ 5 ], which required federal agencies under Labor, Health and Human Services, and Education with research budgets of $100 million or more to provide public online access to peer-reviewed publications within 12 months of the official data of publication [ 6 ]. To comply with federal law, both NIH and NSF have implemented public access policies to make research supported by funding from these federal agencies freely available to the public. The enforcement of these policies typically occurs during the submission of annual and final reporting process for funded grants from NIH and NSF. In these reports, all peer-reviewed publications must be registered through the required mechanism by the specific federal funding agency. For NIH, peer-reviewed publications must be registered with PubMed Central and have a PubMed Central ID (PMCID). For NSF, peer-reviewed publications must be submitted to the NSF Public Access Repository (NSF-PAR) via Research.gov in the form of an archival PDF (PDF/A) [ 7 ]. Unreported and/or incorrectly reported papers can result in delayed acceptance of annual and final reports and funding delays for current and new research grants. Therefore, timely reporting of every peer-reviewed publication is required. For large collaborative research efforts involving large research teams or even multiple research teams, the tracking and proper registration of peer-reviewed publications along with generation of accurate annual and final reports can create a large administrative burden. With large collaborative teams, it is easy for these administrative tasks to be overlooked, forgotten, or lost in the shuffle.

In an effort to help researchers and their minders stay up-to-date with the reporting of peer-reviewed publications, we created the Academic Tracker software package. Written in the Python 3 programming language, Academic Tracker comprehensively searches major peer-reviewed publication tracking web portals, gathering relevant publications and useful tracking characteristics, for example, an indication of whether the publication has been reported to the NIH (is on PubMed), needs to be reported (is associated with an NIH grant), or satisfies the NIH’s requirements to have a PMCID. It has the ability to search PubMed [ 2 ], ORCID [ 8 ], Google Scholar [ 9 ], and Crossref [ 10 ], given a list of authors and/or author IDs. Academic Tracker provides highly customizable reporting templates so information about the resulting publications is easily transformed into appropriate formats for tracking and reporting purposes.

ORCID (Open Researcher and Contributor ID) is a non-profit organization dedicated to uniquely identifying individuals who participate in research [ 8 ]. Once an author is registered, ORCID provides a unique ID that can be used to associate an author with their publications. These associations can be easily accessed from the ORCID website or through their application programming interface (API). Google Scholar is a search engine for scholarly literature with similar API search facilities to ORCID [ 9 ]. Authors can create profiles on Google Scholar, which Google Scholar uses to automatically associate publications with. Crossref is a non-profit association with both commercial and non-profit publisher members with a primary purpose of enabling cross-publishing citation linking [ 10 ]. Crossref’s stated goal is to make “research objects easy to find, cite, link, assess, and reuse.” For the purposes of Academic Tracker, Crossref serves as a database with an easily accessible API for finding relevant publications.

Academic Tracker has three main use-cases and one supportive use-case. The first main use-case searches the aforementioned web portals for publications, given a list of authors. The second main use-case searches PubMed and Crossref for publication information, given a list of publication citations. Neither ORDID nor Google Scholar can be searched for specific publication information directly. ORCID is organized around author profiles and not publications themselves and does not provide a search option by publication characteristics. Google Scholar cannot be searched by specific publication characteristics, because Google Scholar has limited the repetitive programmatic use of their web service in this way. However, Google Scholar does allow repetitive programmatic search by author profile ID. The third main use-case finds collaborators given a list of authors. This is similar to the first use-case, but focuses on compiling the co-authors from the publications rather than the publications themselves. The fourth supportive use-case searches ORCID or Google Scholar for authors’ unique IDs for these sources, given a list of authors.

The main output from the three main use-cases is a Javascript Object Notation (JSON) file containing information about each publication found. Other outputs vary on user settings. Customizable summary and project reports can be created with an option of emailing them as attachments. The collaborator report of the third use-case is also customizable. All emails are also copied into a JSON file. A configuration JSON file is needed as part of the input to Academic Tracker and the fourth supportive use-case will update this file with the information found during the search. A use-case diagram for Academic Tracker is shown in Fig 1 .

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

The first and third use-cases, publication search and collaborator search, are illustrated via the “Publication Search by Author” option. The second use-case, publication information, is illustrated via the “Publication Search by Reference” option. The supporting use-case, ORCID ID and Google Scholar ID searches, are illustrated by the “Unique ID Search” option.

https://doi.org/10.1371/journal.pone.0277834.g001

3 rd party packages

Academic Tracker leverages many third-party Python libraries and packages to accomplish its major tasks. Academic Tracker uses the docopt library to implement a command line interface (CLI) from a Python docstring description. Next, Academic Tracker uses the jsonschema library to validate user JSON input against an expected schema, which is also in JSON format. JSON Schema is an independently developed vocabulary or framework created for the purpose of validating and annotating JSON. Other developers have implemented the vocabulary in several languages, and the jsonschema library is the Python language implementation. The specific schema used in Academic Tracker are in the Validation_Schemas directory of the supplemental materials. Academic Tracker also uses four different packages to query data sources for publications. Specifically, Academic Tracker uses the pymed, habanero, orcid, and scholarly libraries to query PubMed, Crossref, ORCID, and Google Scholar, respectively. For the second use-case, Academic Tracker uses the requests library to make HTTP requests and the beautifulsoup4 library to parse HTML in the pulled web pages given as the reference file. Next, Academic Tracker uses the fuzzywuzzy library to fuzzy match publication titles, which is necessary because publications do not have a universal unique identifier. For general file input/output, Academic Tracker uses several packages, including: i) the python-docx library to read Microsoft Word files, specifically for the reference file input; ii) the pandas library to read and write tabular data, specifically to read in author data and write out reports; and iii) indirectly the openpyxl library, which is used by pandas to write Excel files. In order to comprehensively compare publication information across different runs to see if any information has changed, Academic Tracker uses the deepdiff library. A list of packages and their versions are in Table 1 .

https://doi.org/10.1371/journal.pone.0277834.t001

Although there are 3 main use-cases and 1 supportive use-case, Academic Tracker has 2 main commands and 6 supporting commands ( Table 2 ). The first and third main use-cases are handled by the author_search command, while the second main use-case is handled by the reference_search command. The supportive use-case is handled by the find_ORCID and find_Google_Scholar commands. The remaining four commands help users experiment with the tokenization and reporting systems in Academic Tracker and make it a little easier to convert author information into JSON format. The commands are listed in Table 2 . The input and output files for each command are further described in Table 3 .

https://doi.org/10.1371/journal.pone.0277834.t002

https://doi.org/10.1371/journal.pone.0277834.t003

Module description

Although Academic Tracker is primarily designed to be a command line tool, it does provide an equivalent API, which can be utilized if so desired. The CLI and highest-level API for each command are implemented in the __main__.py file, but other submodules break down the steps into smaller pieces. Utilizing the API, reference_search and author_search are almost completely separated into their own submodules. The athr_srch_modularized.py submodule compartmentalizes the steps of author_search, while the athr_srch_webio.py and athr_srch_emails_and_reports.py submodules contain the functions to interface with the internet and generate reports and emails respectively. reference_search is organized the same way with the ref_srch_modularized.py, ref_srch_webio.py, and ref_srch_emails_and_reports.py submodules. The user_input_checking.py submodule contains the functions to validate user input for errors, and the tracker_schema.py submodule works in tandem with it to store the JSON schema being used for validation. The fileio.py submodule contains all the functions for reading and writing files. The webio.py submodule contains functions to interface with the internet that are more general purpose or common to multiple commands. It is where the functions to interface with the internet for find_ORCID and find_Google_Scholar are. The helper_functions.py submodule contains functions with common operations across all commands that don’t classify well into any other submodule, such as regex operations and data transformation. The citation_parsing.py submodule contains all the functions used to tokenize the reference sources for reference_search. Table 4 lists the submodules of Academic Tracker, and Fig 2 shows a module diagram.

Submodule and module dependencies are illustrated by connecting lines, except for helper_functions which is utilized by most other submodules.

https://doi.org/10.1371/journal.pone.0277834.g002

https://doi.org/10.1371/journal.pone.0277834.t004

The Academic Tracker package was originally developed in a Linux operating system (OS) environment, but has been directly tested on Linux, Windows, and MacOS operating systems. All use-cases have been tested on these operating systems; however, Academic Tracker relies on sendmail or an emulator being installed and configured on the machine for its email functionality. In addition, each submodule includes unit-tests that test all critical functions of the submodule. Every function in every module is tested to make sure it gives the expected output when it should and errors when it should. All requests to web portals are replaced with mock data. The user_input_checking.py submodule has the largest number of tests, since it tests several error states for each element of the input JSON files. Every command line option is tested, for example, silent and not searching ORCID options. Various ways of creating reports are also tested, such as creating a tabular report versus a text report, Excel versus CSV format, and renaming the report from the default name. Several different citation styles and sources are also tested to make sure they are tokenized correctly, such as MEDLINE, a MyNCBI bibliography URL, and an NSF Award page.

Academic Tracker can be utilized in many different ways and was designed with a great deal of flexibility, anticipating users’ desire to use it in unpredictable ways. However, the three main and one supportive use-case are presented here. Note that the figures here are general examples with mostly dummy data. There are full examples with real data and run commands in the supplemental materials (Example_Runs subdirectory). The first main use-case involves searching for publications given author information. Fig 3 shows an example input configuration JSON file, the command line for its execution, the API execution equivalent, and the resulting output files. Fig 4 shows the contents of these resulting output files. Authors without unique ORCID or Google Scholar IDs are identified by matching first name, last name, and at least one affiliation.

Example configuration file, command-line execution, API execution, and file output of the author_search for publications use-case shown.

https://doi.org/10.1371/journal.pone.0277834.g003

Example JSON publications output and plain-text summary report from the author_search for publications use-case shown.

https://doi.org/10.1371/journal.pone.0277834.g004

The second main use-case involves looking for publications based on a given reference. Fig 5 shows an example input configuration JSON file, the command line for its execution, the API execution equivalent, and the resulting output files. Figs 6 and 7 show the contents of the resulting output files.

Example configuration file, reference file, command-line execution, API execution, and file output of the reference_search use-case shown.

https://doi.org/10.1371/journal.pone.0277834.g005

https://doi.org/10.1371/journal.pone.0277834.g006

https://doi.org/10.1371/journal.pone.0277834.g007

The third use-case is basically identical to the first, but a collaborator report attribute needs to be added to an author. Fig 8 is essentially the same as Fig 3 , but with a collaborator report attribute added to Author1 and the report in the output directory. Fig 4 already shows the contents of the publications JSON and summary report. Table 5 shows the contents of the resulting collaborator report table.

Example configuration file, command-line execution, API execution, and file output of the author_search for collaborators use-case shown.

https://doi.org/10.1371/journal.pone.0277834.g008

https://doi.org/10.1371/journal.pone.0277834.t005

The supportive use-case is broken into 2 commands: find_ORCID for finding ORCID IDs and find_Google_Scholar for finding Google Scholar IDs. Fig 9 shows an example input configuration JSON, how to accomplish this using the command line and API, and the resulting output files for finding ORCID IDs. Fig 10 shows the contents of the resulting configuration JSON file. Figs 11 and 12 are the same as Figs 9 and 10 but for finding Google Scholar IDs.

Example configuration file, command-line execution, API execution, and file output of the author ORCID ID search use-case shown.

https://doi.org/10.1371/journal.pone.0277834.g009

https://doi.org/10.1371/journal.pone.0277834.g010

Example configuration file, command-line execution, API execution, and file output of the author Google Scholar ID search use-case shown.

https://doi.org/10.1371/journal.pone.0277834.g011

https://doi.org/10.1371/journal.pone.0277834.g012

Discussion and conclusions

Academic Tracker is a useful tool for querying major scientific publication web portals for publications, given a list of authors or references and for creating highly customizable reports from the list of publications found. The software package provides assistance in repetitive tracking and reporting of peer-reviewed publications associated with specific authors, projects, and grants. Specifically, the JSON configuration file supports batch execution, directing Academic Tracker to perform multiple related author searches and report generations. The JSON configuration file has many optional parameters to customize searching and report generation, including a cutoff_year for searching. Academic Tracker is also designed for repetitive tracking by comparing current search results to prior search results to limit reporting to changes in publications detected and in publication attributes. Academic Tracker also provides facilities for generating lists of co-author collaborators, which has several uses in grant proposal submission. But given the number of major use-cases and versality of the software, there is some intellectual overhead required to initially setup the JSON configuration file and customize reports. Additional supportive commands are included to make learning and troubleshooting the tool easier for new users. Also, there is extensive documentation available to help with the learning curve: https://moseleybioinformaticslab.github.io/academic_tracker/

In addition, when installed via the Python package management system pip, a console script “academic_tracker” is created automatically for the user, providing easy access to the CLI.

While the package accesses multiple major peer-reviewed publication tracking web portals, it is fundamentally limited to the information provided by these web portals and must assume the information provided is accurate. One possibility is to download a PDF of the publication itself for analysis. However, this is pragmatically infeasible, since there is wide variation in how journals organize the splash page of their publications. One way to alleviate this issue is for journals to adopt a DOI extension like “.pdf” which would link directly to the PDF version of the publication, if the PDF version is accessible. This is similar to the versioning “.v#” DOI extension that FigShare uses to provide access each version of a public FigShare repository. If a practical way to directly access the PDF is implemented either by journals or the publication tracking web portals, we would extend Academic Tracker to utilize it. Still in its current implementation, we believe Academic Tracker can significantly reduce the stress and hassle of reporting publications to federal funding agencies, reducing the chance for accidental non-compliance and resulting delay in funding.

Acknowledgments

We also thank Jennifer Moore for feedback during the development of the report generation capabilities.

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